Domain generalisation methods have shown to be effective in semantic image segmentation tasks in areas such as autonomous driving. Thus, in this paper we draw inspiration from this previous work [1]} to propose a new loss function that utilizes traditional superpixel segmentation using the Simple Linear Iterative Clustering (SLIC) method, a k-means-based technique [2]} to enforce a cluster-based consistency during training, generating more geometrically regular predictions that are better translated to other datasets without requiring any changes to the base model. Our contributions herein are two-fold: i) SUPRA (SUPeRpixel Augmented), a framework that generates the superpixels and incorporates our loss to a model and ii) SLICLoss, a loss that combines agreement with superpixel boundaries generated by SLIC, which is then added to the BCE loss function.
